10 MCP servers to connect LLMs with databases

Amazon Web Services has released official Model Context Protocol (MCP) servers for Amazon Aurora MySQL, Aurora PostgreSQL, and Aurora DSQL, enabling natural language database queries and administration. The servers convert plain language commands into SQL queries, allowing developers and AI agents to perform lookups, updates, and schema-aware code generation without writing SQL manually. These MCP servers join a growing ecosystem of vendor-backed connectors for relational, NoSQL, graph, vector, and data warehouse platforms, compatible with tools like Claude Code, Cursor, and VS Code.

Model Context Protocol https://www.infoworld.com/article/4029634/what-is-model-context-protocol-how-mcp-bridges-ai-and-external-services.html MCP has gained considerable momentum as a standard connector between LLM-powered tools and local systems, internal and external APIs, and data sources. From major clouds https://www.infoworld.com/article/4129024/five-mcp-servers-to-rule-the-cloud.html to devops tools https://www.infoworld.com/article/4096223/10-mcp-servers-for-devops.html , MCP servers are enabling powerful, AI-powered development and operations capabilities through natural language commands. Nowhere is this more true than in the world of databases. Most major database platforms now support agentic access through MCP servers. Using an MCP server for databases, you and your AI agent proxies can perform lookups, create and update data, and perform administrative tasks without you having to write SQL by hand. The MCP server could also guide your LLMs to write new code or build automations that align with your database schema, like its tables, structure, and fields, as well as embeddings, indexes, and metadata. It could also aid debugging by enabling faster queries to surface data issues or misconfigurations, along with plenty of other possible use cases. Below, we’ll cover official MCP servers from some of the top platform options across major database styles. Though maturity varies, the MCP servers discussed below represent some of the best vendor-backed offerings available today across relational SQL https://www.infoworld.com/article/4140734/the-revenge-of-sql-how-a-50-year-old-language-reinvents-itself.html , NoSQL https://www.infoworld.com/article/2260280/what-is-nosql-databases-for-a-cloud-scale-future.html , graph https://www.infoworld.com/article/2265778/what-is-a-graph-database-a-better-way-to-store-connected-data.html , vector https://www.infoworld.com/article/2335281/vector-databases-in-llms-and-search.html , and data warehouse https://www.infoworld.com/article/2268778/what-is-a-data-warehouse-the-source-of-business-intelligence.html systems. These servers can be used by any MCP-compatible tool, IDE, or agent, whether it’s Claude Code, Codex, Cursor, Gemini CLI, Google Antigravity, VS Code, Windsurf, or something else. Adding them is typically simple, often involving a lightweight JSON addition to your MCP configuration file. MySQL https://www.mysql.com/ and PostgreSQL https://www.postgresql.org/ are the world’s most widely-used open source https://www.infoworld.com/article/2262355/what-is-open-source-software-open-source-and-foss-explained.html databases. However, in both cases, there is no canonical MCP server—what we see are different MCP servers emerging across vendors. One of these vendors is Amazon Web Services AWS , which offers official MCP servers for Amazon Aurora, its managed relational database service compatible with both MySQL and PostgreSQL. According to the documentation on GitHub https://github.com/awslabs/mcp/tree/main/src/mysql-mcp-server , the Amazon Aurora MySQL MCP Server https://awslabs.github.io/mcp/servers/mysql-mcp-server can be used to convert natural language commands into MySQL-compatible SQL queries, which can then be executed against Aurora MySQL databases. Similarly, the Aurora Postgres MCP Server https://github.com/awslabs/mcp/tree/main/src/postgres-mcp-server provides MCP tools for working on PostgreSQL databases. The Aurora DSQL MCP Server https://github.com/awslabs/mcp/tree/main/src/aurora-dsql-mcp-server does the same for distributed Postgres databases. AWS provides a growing portfolio of official MCP servers https://github.com/awslabs/mcp across its product line, including MCP servers for other Amazon database platforms like DynamoDB https://awslabs.github.io/mcp/servers/dynamodb-mcp-server , ElastiCache https://awslabs.github.io/mcp/servers/elasticache-mcp-server , and Redshift https://awslabs.github.io/mcp/servers/redshift-mcp-server . If you’re a heavy AWS shop and you want to enable LLM interactions with your data, these are sensible choices. BigQuery is Google’s cloud-based data analytics platform, and a popular data source for AI applications. BigQuery users with API access configured can also utilize the BigQuery MCP Server https://docs.cloud.google.com/bigquery/docs/use-bigquery-mcp to interact with the platform using MCP-compatible AI clients. Using the remote BigQuery MCP Server https://docs.cloud.google.com/bigquery/docs/use-bigquery-mcp , engineers can generate and execute queries on data sources, or return metadata on datasets, tables, and schema. This can be done with a simple natural language prompt like “List the datasets in project PROJECT ID .” Results are filterable by region, data set ID, column name, and more. As part of Google’s fully-managed, remote-hosted MCP portfolio, the BigQuery MCP Server provides some peace of mind regarding security, maintenance, and ease of use for distributed teams. The MCP tools are subject to some limitations, however, in terms of query result size, processing time, and other factors. If you’re using BigQuery and you want more agentic control, you’ll want to check this one out. Another important database category includes platforms designed for keyword and semantic search. In this space, Elasticsearch https://www.elastic.co/elasticsearch is commonly deployed. Instead of providing a single MCP server, Elasticsearch provides the Elastic Agent Builder https://www.elastic.co/docs/explore-analyze/ai-features/elastic-agent-builder , which is a more comprehensive framework aimed at agentic workflows. Using Elastic Agent Builder, you can chat with an agent to retrieve data context from Elasticsearch data and extend it into various environments. The Agent Builder itself includes an MCP server https://www.elastic.co/docs/explore-analyze/ai-features/agent-builder/mcp-server endpoint for programmability and exposing the agent to other clients. Unlike others on this list, this is not a direct MCP interface to raw Elasticsearch APIs. Instead, it’s an interface that exposes skills from the agent platform. This should also not be confused with the Elasticsearch MCP Server https://github.com/elastic/mcp-server-elasticsearch/releases , released in mid-2025, which has since been deprecated. A possible downside of using this utility is that it includes an additional layer between your IDE or agent and the data you’re searching. The agent setup requires a higher subscription and takes additional steps to configure compared to other MCP servers. That said, if you want an extensible common layer to interact with both Elasticsearch and external MCP servers, while centralizing responsibilities like permissions, this is an interesting proposition. Graph databases are another key NoSQL database type these days, specializing in using nodes and edges to accelerate queries of highly interconnected data. Of these, Neo4j https://neo4j.com/product/neo4j-graph-database/ is a popular graph database option. The Official MCP Server for Neo4j https://neo4j.com/developer/genai-ecosystem/model-context-protocol-mcp/ works with all kinds of Neo4j deployment desktop, sandbox, self-managed, and the managed Neo4j Aura cloud service , and allows LLM-based clients to retrieve graph schema, execute read and write statements, execute graph algorithms, and more. In addition, several other MCP servers for Neo4j are available from Neo4j Labs https://github.com/neo4j-contrib/mcp-neo4j . These have specialized uses, such as generating Cypher queries from natural language, maintaining an in-memory graph database, modeling and visualizing graph, and interacting with the Neo4j Aura API. The first MCP server for Neo4j was developed in December 2024. If you’re an avid Neo4j user and want to experiment with interacting with your graph databases in a chat-infused way, these stand as an interesting platform of servers. Google’s MCP Toolbox for Databases https://github.com/googleapis/mcp-toolbox is a notable MCP server because it’s a popular catch-all for various database types. Unlike the other entries on this list, this server connects LLMs not to a single managed database, but unifies LLM access to multiple systems. The open source utility ships with pre-built configurations https://mcp-toolbox.dev/documentation/configuration/prebuilt-configs/ for nearly 30 databases including PostgreSQL, MySQL, SQL Server, Oracle Database, MongoDB, Redis, Neo4j, and Snowflake, as well as the databases in Google Cloud. Once you define data sources in a tools.yaml file, you can use MCP Toolbox to perform structured queries or semantic searches against databases directly from within an IDE or agentic client using plain English. MCP tools translate commands into actions like list tables and execute sql . MCP Toolbox for Databases is mature, originally built as a generative AI utility and later re-worked for MCP-style workflows. It offers numerous download, configuration, and interaction methods. If you’re using a variety of databases on Google Cloud and elsewhere and you want an “all-in-one” MCP server, MCP Toolbox for Databases is a great place to start. MongoDB https://www.mongodb.com/ is the popular NoSQL document-oriented database. The creators of MongoDB have released an official MCP server https://github.com/mongodb-js/mongodb-mcp-server that works with the open-source database as well as the company’s cloud-hosted MongoDB Atlas database platform. The MongoDB MCP Server provides a number of tools https://github.com/mongodb-js/mongodb-mcp-server tool-list to interact with MongoDB. You can query the database, return information on collections, create or remove collections or indexes, gather statistics on database usage, and more. Other tools enable MongoDB Atlas operations, like creating users or clusters, returning cluster data, and other functions. The server’s tools are read-only by default but can be switched to allow write capabilities. It can be used locally, but also supports Streamable HTTP transport for remote servers, although that comes with greater security concerns. For those using MongoDB and wanting to hook their AI-enabled IDE or CLI up with more automated powers, the official MongoDB MCP Server is worth checking out. Among vector-native databases https://www.infoworld.com/article/4060211/do-vector-native-databases-beat-add-ons-for-ai-applications.html , Pinecone https://www.pinecone.io/ stands as a strong, widely used option with a well-designed API https://docs.pinecone.io/reference/api/introduction and comprehensive SDKs. The Pinecone MCP server https://docs.pinecone.io/guides/operations/mcp-server extends this experience, allowing users to query its documentation and execute functionality via AI agents and AI-enabled IDEs. To date, the Pinecone MCP server consists of nine MCP tools. These cover read-only actions, like knowledge gathering via the Pinecone official documentation and querying vector records, index metadata, configurations, and statistics. It also allows for write operations like updating records and creating new indexes. Released in mid-2025, the Pinecone MCP server is one of the more complete early implementations, with easy configuration and installation. For those using Pinecone who want to test new LLM-assisted workflows for creating indexes with embeddings, performing reranking, or testing results using natural language commands, the Pinecone MCP server is worth trying out. An ultra-fast in-memory database, Redis is commonly used for caching, real-time analytics, and other latency-sensitive use cases. And as you might have guessed, the company behind the Redis database provides an official MCP server https://redis.io/docs/latest/integrate/redis-mcp/ . The server allows read, query, and write capabilities. Developers can use Redis MCP from an LLM client to perform high-level actions to analyze, reference, or embed Redis data and interact with the Redis server within their prompts. The documentation suggests some example prompts for common use cases, such as “Cache this item,” “How many keys does my database have?,” and “What is user:1’s email?” Unlike other MCP servers, which only allow a slice of platform capabilities, Redis MCP offers full Redis support. This enables working with Redis constructs such as hashes, lists, sets, sorted sets, streams, and more, according to the GitHub repository https://github.com/redis/mcp-redis . One possible drawback is that Redis MCP has yet to support Streamable HTTP transport. Until this is developed, the server is constrained to local deployment. But for those seeking a local MCP server to work with Redis data, this is the best choice. Snowflake is a cloud-hosted, AI-enabled data platform widely used in enterprise contexts for data warehousing, data analytics, and data engineering purposes. Compared to other data storage systems, Snowflake is unique in that it’s more fully managed and combines structured and non-structured data types. The Snowflake MCP Server https://www.snowflake.com/en/developers/guides/getting-started-with-snowflake-mcp-server/ , available on GitHub https://github.com/Snowflake-Labs/mcp , can be used to perform many of the standard Snowflake platform operations. This includes a “fuzzy” search of all records via Snowflake’s Cortex Search and structured data semantic lookups using Cortex Analyst. Other abilities include object management operations like creating, updating, and deleting records. The server also can invoke other agentic-designed capabilities, like the ability to generate and execute SQL statements against back-end databases. Snowflake MCP Server is well-thought-out and well-documented, with walkthroughs for various agent and deployment patterns. Those already building with Snowflake should find it complements the mechanics they already employ. A longtime open-source favorite, PostgreSQL https://www.postgresql.org/ is one of the most popular and trusted object-relational SQL-based database systems. With an active open source community https://leaddev.com/technical-direction/postgresql-database-quietly-ate-world , Postgres has been maturing for decades. Given its open source nature, there isn’t a single “official” MCP server for the platform. Anthropic built an original reference implementation, but it’s now archived https://github.com/modelcontextprotocol/servers-archived/tree/main/src/postgres . Instead, database platforms built on PostgreSQL provide different flavors https://dbhub.ai/blog/state-of-postgres-mcp-servers-2025 of MCP servers, with a range of vendor neutrality and specificity. One notable option is the Supabase MCP Server https://github.com/supabase-community/supabase-mcp database , provided by Supabase https://supabase.com/mcp , a cloud-based “back end as a service” and Postgres development platform. Supabase MCP Server connects AI agents with Supabase projects, allowing engineers to issue natural language commands to manage tables, query data, get logs, fetch configuration information, and more. The Supabase MCP Server is pre-1.0 release and some features are still experimental. If you’re an engineer using Supabase and looking for an MCP server to connect your AI assistant with your Postgres databases, this is a good tool to test out. So far, we’ve reviewed official, vendor-backed MCP servers from some of the most-adopted managed databases. However, numerous MCP servers exist across other database platforms and types. One MCP server that aggregates LLM access across various database types is DBHub https://github.com/bytebase/dbhub , which works with MySQL, PostgreSQL, SQL Server, MariaDB, and SQLite. Developed by Bytebase https://www.bytebase.com/ , DBHub is described as a zero-dependency, token-efficient MCP server. For SQL, the options are nearly endless. Official servers exist for Microsoft Azure SQL https://devblogs.microsoft.com/azure-sql/introducing-sql-mcp-server/ and DuckDB https://github.com/motherduckdb/mcp-server-motherduck . PulseMCP catalogs more than 100 MCP servers https://www.pulsemcp.com/servers?q=mysql for MySQL https://dev.to/benborla/mcp-server-for-mysql-3jf1 main-content , although most are unofficial, solo-creator open source projects. Of these, one of the most starred is MCP Server for MySQL https://github.com/benborla/mcp-server-mysql , developed by full-stack developer Ben Borla https://benborla.dev/ and optimized for Claude Code. For Postgres, notable alternatives to Supabase include pgEdge Postgres MCP https://github.com/pgEdge/pgedge-postgres-mcp/ , Neon MCP server https://neon.com/docs/ai/neon-mcp-server , and Postgres MCP Pro https://github.com/crystaldba/postgres-mcp . For vector databases, others beyond Pinecone have been quick to adopt MCP as well, including Weaviate https://docs.weaviate.io/weaviate/mcp/docs-mcp-server and Milvus https://milvus.io/docs/milvus and mcp.md . Before diving into MCP servers for enterprise databases, it’s important to understand the security risks. For instance, prompt injection remains an unsolved problem https://dbhub.ai/blog/state-of-postgres-mcp-servers-2025 , so it’s recommended to limit permissions for SQL statements. To mitigate this, Supabase recommends https://github.com/supabase-community/supabase-mcp security-risks enabling AI client settings that require manual approval for each tool call before execution. Experts also recommend assigning only the minimum permissions required and avoiding exposure of sensitive data like API credentials. Due diligence around authentication and authorization is especially important when hosting remote servers. Lastly, to avoid shadow IT, it’s becoming common practice to catalog the internal MCP servers you use, even for experimental projects. For this, experts recommend an MCP registry https://www.infoworld.com/article/4145014/how-to-build-an-enterprise-grade-mcp-registry.html that documents approved servers. An MCP registry improves both MCP server discovery and security awareness.